Styx: Transactional Stateful Functions on Streaming Dataflows
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866911023523430400 |
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| author | Psarakis, Kyriakos Christodoulou, George Siachamis, George Fragkoulis, Marios Katsifodimos, Asterios |
| author_facet | Psarakis, Kyriakos Christodoulou, George Siachamis, George Fragkoulis, Marios Katsifodimos, Asterios |
| contents | Developing stateful cloud applications, such as low-latency workflows and microservices with strict consistency requirements, remains arduous for programmers. The Stateful Functions-as-a-Service (SFaaS) paradigm aims to serve these use cases. However, existing approaches provide weak transactional guarantees or perform expensive external state accesses requiring inefficient transactional protocols that increase execution latency.
In this paper, we present Styx, a novel dataflow-based SFaaS runtime that executes serializable transactions consisting of stateful functions that form arbitrary call-graphs with exactly-once guarantees. Styx extends a deterministic transactional protocol by contributing: i) a function acknowledgment scheme to determine transaction boundaries required in SFaaS workloads, ii) a function-execution caching mechanism, and iii) an early-commit reply mechanism that substantially reduces transaction execution latency. Experiments with the YCSB, TPC-C, and Deathstar benchmarks show that Styx outperforms state-of-the-art approaches by achieving at least one order of magnitude higher throughput while exhibiting near-linear scalability and low latency. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2312_06893 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Styx: Transactional Stateful Functions on Streaming Dataflows Psarakis, Kyriakos Christodoulou, George Siachamis, George Fragkoulis, Marios Katsifodimos, Asterios Distributed, Parallel, and Cluster Computing Databases Developing stateful cloud applications, such as low-latency workflows and microservices with strict consistency requirements, remains arduous for programmers. The Stateful Functions-as-a-Service (SFaaS) paradigm aims to serve these use cases. However, existing approaches provide weak transactional guarantees or perform expensive external state accesses requiring inefficient transactional protocols that increase execution latency. In this paper, we present Styx, a novel dataflow-based SFaaS runtime that executes serializable transactions consisting of stateful functions that form arbitrary call-graphs with exactly-once guarantees. Styx extends a deterministic transactional protocol by contributing: i) a function acknowledgment scheme to determine transaction boundaries required in SFaaS workloads, ii) a function-execution caching mechanism, and iii) an early-commit reply mechanism that substantially reduces transaction execution latency. Experiments with the YCSB, TPC-C, and Deathstar benchmarks show that Styx outperforms state-of-the-art approaches by achieving at least one order of magnitude higher throughput while exhibiting near-linear scalability and low latency. |
| title | Styx: Transactional Stateful Functions on Streaming Dataflows |
| topic | Distributed, Parallel, and Cluster Computing Databases |
| url | https://arxiv.org/abs/2312.06893 |